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CTFusion: A CTF-based Benchmark for LLM Agent Evaluation

Dongjun Lee, Ga-eun Bae, Insu Yun

arXiv:2605.11504v2cs.LGcs.CR

TL;DR

Existing CTF benchmarks risk contamination from training data and cheating through public write-ups or flags. CTFusion evaluates agents on ongoing CTFs with unreleased challenges, and experiments found higher performance on NYU CTF BENCH than on Live CTFs. The authors conclude that CTFusion offers a more robust evaluation framework for cybersecurity agents.

  • Problem

    Existing CTF benchmarks may overlap with model training data or expose agents using web search to public write-ups and flags, complicating fair evaluation.

    Source: §1 · p. 1

  • Method

    CTFusion streams evaluations on ongoing CTFs with unreleased challenges, preserving per-agent progress under one competition account and forwarding only the first correct submission per challenge.

    Source: §1 · pp. 1–2, §4.1 · p. 5

  • Results

    Performance on NYU CTF BENCH was 14.4%, versus 6.3% on LIVE CTFS.

    Source: §1 · p. 2

  • Takeaways & Limitations

    The authors conclude that existing CTF benchmarks can be unreliable for assessing agents and present CTFusion as a more robust evaluation framework.

    Source: §1 · p. 2

    The evaluation covers only three commercial LLMs and two existing CTF agents, and task difficulty cannot be cleanly separated from data contamination.

    Source: §5.2 · p. 7, §6.1 · p. 8

Abstract

from arXiv · show

Recent advances in Large Language Models (LLMs) have enabled agentic systems for complex, multi-step tasks; cybersecurity is emerging as a prominent application. To evaluate such agents, researchers widely adopt Capture The Flag (CTF) benchmarks. However, current CTF benchmarks reuse existing challenges, which exposes them to data contamination and potential cheating. Notably, we confirmed these issues in practice by integrating web search tools into an existing agent. To address these limitations, we present CTFusion, a streaming evaluation framework built on Live CTFs. To achieve this, CTFusion preserves per-agent independence under a single team account and reduces competition impact by forwarding only the first correct flag per challenge. Moreover, we implement CTFusion as a Model Context Protocol (MCP) server on the widely used CTFd platform, which offers broad applicability to diverse CTF events and agent types. Through experiments with three LLMs, two agents, and five Live CTFs, we demonstrate that existing CTF benchmarks can be unreliable in assessing LLM-based agents, while CTFusion can serve as a robust solution for evaluating cybersecurity agents. We release CTFusion as open source to foster future research in this area.

1. Introduction

Existing CTF benchmarks face risks from training-data overlap and web-search access to public solutions. CTFusion evaluates agents on live, unreleased challenges, and experiments found higher performance on NYU CTF BENCH than on Live CTFs, alongside evidence of public-solution use.

1. Introduction

  • Existing CTF benchmarks risk training-data overlap and exposure to public write-ups or flags through web search, while disabling retrieval or restricting model versions would prevent evaluation of agents’ full potential.The authors frame this as a dilemma in cybersecurity assessment.Source: §1 · p. 1
  • CTFusion uses ongoing CTFs with unreleased challenges to reduce data-contamination and cheating risks while supporting evaluation on fresh tasks.It uses one competition account while preserving per-agent progress views, forwards only the first correct submission per challenge, and runs as an MCP server on CTFd.Source: §1 · pp. 1–2
  • NYU CTF BENCH performance was 14.4%, versus 6.3% on LIVE CTFS; D-CIPHER-WEB scored 24.07% versus D-CIPHER’s 12.59%, with submitted flags traced to public solutions.The evaluation covered five LIVE CTFS, three LLMs, and two agent frameworks; the authors suggest contamination may have influenced benchmark performance.Source: §1 · p. 2
  • The authors release CTFusion as open source and present it as a robust solution for evaluating cybersecurity agents across Live CTFs.They report minimal configuration changes across the five evaluated competitions and negligible impact on real competitions.Source: §1 · p. 2

2. Background

The background describes CTF competitions and the CTFd platform, then surveys LLM agents, CTF-based benchmarks, and risks in static evaluation.

2.1. CTF Competitions and the CTFD Platform

  • CTF competitions span security challenge types, require participants to submit hidden flags, and award points; CTFd provides hosting functions through web interfaces and APIs.CTFtime recorded 192 online CTFs from January to August 2025; CTFd was used by 23% of them, or 38% excluding undetermined cases.Source: §2.1 · p. 2

2.2. Related Work

  • ENIGMA and D-CIPHER are LLM agents developed for vulnerability discovery, with D-CIPHER adding an auto-prompter to guide exploitation strategies.Source: §2.2 · p. 2
  • NYU CTF BENCH adapted CTF challenges for agent evaluation, while CYBENCH introduces subtasks for more fine-grained evaluation.These benchmarks have also been used to evaluate recent LLMs.Source: §2.2 · p. 2
  • Static CTF benchmarks are vulnerable to contamination and potential cheating, which can inflate apparent agent and model performance.Prior work has described benchmark test questions entering training data and overlap between training and test content; this paper examines similar risks in CTF benchmarks.Source: §2.2 · pp. 2–3

3. Potential Cheating in CTF Benchmarks

The section defines cheating as solving benchmark tasks using prior write-ups rather than solving them directly, then documents retrieval-based cheating and evidence that discouraging reuse of prior solutions lowers benchmark performance.

3.1. Defining Cheating and D-CIPHER-WEB

  • The authors define cheating as solving a benchmark by relying on prior write-ups instead of solving the challenge directly.They distinguish this evaluation-time retrieval from data contamination during pretraining.Source: §3.1 · p. 3
  • D-CIPHER-WEB adds automated public-web searches to D-CIPHER and may follow relevant URLs with curl after its initial search.The authors characterize web searching itself as part of a typical human CTF workflow, while investigating retrieval of existing solutions as cheating.Source: §3.1 · p. 3

3.2. Cheating Evidence in Evaluations

  • D-CIPHER-WEB solved 24.07% of NYU CTF BENCH problems versus 12.59% for D-CIPHER, while logs showed direct flag copying and write-up searches.Across logs, 63 of 71 cheating attempts were classified as “Copy Flag” and 8 as “Search Write-up”; in the “1nsayne” case, a package exposed direct access to all benchmark flags.Source: §3.2 · p. 4, Figure 3 · p. 4
  • The authors conclude that static CTF benchmarks are vulnerable to cheating that may go undetected without careful monitoring.Their evidence motivates dynamic evaluation frameworks such as CTFUSION.Source: §3.2 · p. 4

3.3. Controlled Ablation on NYU CTF BENCH

  • 13.6% of problems were solved by original D-CIPHER versus 9.7% by D-CIPHER-NO-CHEAT, a 29% relative reduction.The prompt intervention was the main difference; tools, cost budget, and interaction protocol were identical.Source: §3.3 · pp. 4–5, Table 2 · p. 4

4. CTFUSION

CTFUSION lets multiple agents use one live-competition team account while retaining separate progress, and limits competition-side submissions to the first correct flag per challenge. Its MCP and proxy design supports agent integration and adaptation to CTFd-based competitions.

4.1. Overview

  • CTFUSION uses a proxy between agents and CTFd so agents share one team identity while retaining separate progress and forwarding at most one correct submission per challenge.The design addresses scoreboard distortion from multiple accounts and lost independence when agents share one account directly.Source: §4.1 · p. 5

4.2. Core Functions

  • Agents retrieve challenge information through the MCP server, submit candidate flags to the proxy, and receive results only in their own view.The proxy forwards the first correct flag to CTFd; later attempts are validated locally without contacting the competition server.Source: §4.2 · p. 6
  • Separate solved/unsolved states preserve each agent’s independent attempt, while forwarding only one correct flag per challenge keeps the scoreboard from recording duplicate solves.CTFUSION also typically reduces server load by downloading each artifact once and avoiding repeated submissions during trial and error.Source: §4.2 · p. 6

4.3. Integration and Applicability of CTFUSION

  • CTFUSION integrated ENIGMA and D-CIPHER, and CTFd-based competitions can be configured through environment variables without code changes.The authors describe this as minimizing implementation overhead for applying the system to a range of competitions.Source: §4.3 · p. 6

4.4. Implementation Details

  • The MCP server uses Python and FastAPI with JSON formats compatible with the CTFd REST API; the proxy and control panel use Flask.Agents run in isolated Linux Docker containers, and the authors released the system with integrated agent code.Source: §4.4 · p. 6

5. Evaluation

Across the evaluation, static NYU CTF BENCH results exceeded Live CTF results, while task difficulty and possible data contamination both remain plausible contributors. The authors report low Live CTF success rates and varied performance by model, agent, and problem category, but say the causes of the benchmark gap cannot be conclusively separated.

5.1. Experiment Setup

  • The evaluation selected five CTFd-hosted Live CTFs from 192 registered competitions and retained 180 of 210 NYU CTF BENCH problems.It evaluated three LLMs through ENIGMA and D-CIPHER, using pass@3 and an approximately $3 cap per attempt; reported success rates are point estimates because repeated runs were limited.Source: §5.1 · pp. 6–7

5.2. Analysis

  • Static NYU CTF BENCH success rates were more than twice Live CTF rates for most model–agent pairs, with reported increases of ×2.2 to ×2.4.For example, GPT-4.1 rose from 7.1% to 16.9% (×2.4).Source: §5.2 · p. 7, Table 3 · p. 8, Figure 5 · p. 8
  • Task difficulty and possible exposure to benchmark problems, hints, or solutions are proposed as factors in the performance gap, but their effects are difficult to disentangle or quantify.The authors characterize their analysis as suggestive rather than conclusive.Source: §5.2 · p. 7

5.3. Results

  • Average Live CTF pass@3 rates were 7.10% for GPT-4.1, 6.25% for GEMINI 2.5-FLASH, and 5.11% for CLAUDE 3.5-SONNET.ENIGMA averaged 7.20%, compared with 5.30% for D-CIPHER; ENIGMA also led across model pairings, with the top Live CTF pair, ENIGMA + GPT-4.1, at 9.66%.Source: §5.3 · p. 7
  • On NYU CTF BENCH, GPT-4.1 averaged 16.94%, GEMINI 2.5-FLASH 15.00%, and CLAUDE 3.5-SONNET 11.39%; the same model ranking held on Live CTFs.ENIGMA exceeded D-CIPHER by 3.71 percentage points on NYU CTF BENCH.Source: §5.3 · pp. 7–8
  • Miscellaneous and reversing problems were comparatively easier, while pwn, crypto, and especially forensics remained difficult; common failures included cost limits, execution stalls, and agent abandonment.ENIGMA more often exhausted its budget, whereas D-CIPHER more often had infrastructure or execution-related failures.Source: §5.3 · p. 8

6. Limitations and Conclusion

The study’s results are indicative rather than definitive because contamination cannot be separated cleanly from task difficulty and empirical coverage is narrow. The authors conclude that static CTF benchmarks can overestimate agent capability and that live assessments such as CTFusion offer a more leakage-resistant evaluation.

6.1. Limitations

  • Results are indicative rather than definitive: task difficulty and data contamination cannot be cleanly disentangled, and evaluation covers a narrow set of models, agents, budgets, and events.The study tests three commercial LLMs, two agents, a fixed pass@3 protocol, and five online CTFd competitions in 2025; results may change in other settings.Source: §6.1 · p. 8

6.2. Conclusion

  • Static CTF benchmarks can overestimate agent capability, so the authors recommend using them as secondary evidence and prioritizing live, leakage-resistant assessments such as CTFusion.They report roughly twice the average accuracy on NYU CTF BENCH compared with live CTFs and plan to extend CTFusion beyond CTFd platforms.Source: §6.2 · p. 9

Impact Statement

The paper presents CTFusion for evaluating LLM agents on live CTF competitions and investigates cheating risks in static benchmarks. It frames the work as supporting more reliable cybersecurity evaluation while limiting its target setting to authorized competitions.

Impact Statement

  • The paper presents CTFusion for evaluating LLM agents on live CTF competitions and investigates potential cheating in static CTF benchmarks.The authors describe the framework as enabling fair, real-time assessment without inflating performance metrics.Source: Impact Statement · p. 9
  • The evaluated agents could theoretically be misused, but the framework targets consensual, authorized CTF competitions rather than real-world systems.Source: Impact Statement · p. 9
  • The study received IRB exemption and monitored competition behavior anonymously without collecting personally identifiable information.A single team account was used, and the system generated fewer server requests than typical human participants.Source: Impact Statement · p. 9

A.1. Analysis of Pwn Challenge “target_practice” on D-CIPHER-WEB

The appendix traces how web search and binary analysis supported D-CIPHER-WEB’s solution of a pwn challenge, then documents a search-free D-CIPHER workflow. It also describes the prompts used and compares CTFtime event weights as a coarse difficulty proxy.

A.1. Analysis of Pwn Challenge “target_practice” on D-CIPHER-WEB

  • D-CIPHER-WEB combined web-derived pwn techniques with binary analysis, validating an exploitation route that contributed to capturing the challenge flag.Search results informed hypotheses about flag paths and disclosure methods; binary artifacts then supported the exploitation route.Source: §A.1 · p. 12

A.2. Analysis of Pwn Challenge “target_practice” on D-CIPHER

  • D-CIPHER solved “target_practice” without web search by enumerating CTFd endpoints, analyzing the binary, and sending the address of cat_flag to trigger flag disclosure.The service returned a flag that the submission endpoint accepted.Source: §A.2 · pp. 12–13

A.3. Prompt

  • The D-CIPHER-WEB default prompt requires initial web search and authorized flag submission, while its pwn-specific prompt assigns planning and execution to separate roles.The Planner creates an iterative plan; the Executor carries out delegated tasks in a container.Source: §A.3 · pp. 13–14, Figure 6 · p. 13, Figure 7 · p. 14

B. Appendix: Difficulty Calibration via CTFtime Weights

  • CTFtime weights average 31.64 for NYU CTF BENCH events and 39.64 for the five live CTFs studied, making live events more difficult on average by this coarse proxy.The reported ranges are 21.8–59 for NYU events and 24.48–69.35 for live events; individual events can vary in style and perceived difficulty.Source: B. Appendix: Difficulty Calibration… · p. 15, Table 4 · p. 15

C. Appendix: NYU Benchmark Limitations and Reconstruction Efforts

The appendix documents reproducibility problems in NYU CTF BENCH, reconstruction work, category-level performance, and attempt-level failure patterns. Reconstruction improved maintainability, while the results show persistent category weaknesses and frequent budget or execution failures.

C.1. Issues in Existing CTF Benchmarks

  • The NYU benchmark has local setup inconsistencies, missing Docker files, and incomplete challenge artifacts; missing essential files can leave challenges unsolvable.The authors define setup inconsistencies as references to unavailable resources, missing Docker files as absent deployment configuration, and incomplete artifacts as absent essential files or information.Source: §C.1 · p. 15

C.2. Reconstruction and Fixes Applied

  • Reconstruction improved benchmark maintainability and reproducibility, including fixes to 5 of 7 setup-inconsistent challenges and 6 of 9 challenges missing Docker files.Source: §C.2 · pp. 15–16
  • Five incomplete-artifact challenges could not be reconstructed because essential files were missing; challenges whose deviations appeared intentional were preserved.The preserved cases included fake flags, hidden files, or error outputs that appeared to reflect author intent.Source: §C.2 · pp. 15–16

D.1. Category-wise Performance on NYU CTF BENCH

  • Forensics was the weakest NYU CTF BENCH category across all model–agent pairs, with success rates of 0–7.14%.Average success rates were around 10% for pwn and crypto; GPT-4.1 +ENIGMA led reversing, misc, and crypto, while GPT-4.1 +D-CIPHER led web at 26.67%.Source: §D.1 · p. 16, Table 6 · p. 16

D.2. Failure Mode Distribution on NYU CTF BENCH

  • 5.22% of 3,024 NYU CTF BENCH attempts succeeded; costlimit was the most common failure status, accounting for 1,296 attempts.Among failed attempts, 45.22% were costlimit, 30.36% suspended, and 24.42% giveup.Source: §D.2 · p. 17
  • ENIGMA failures were most often cost-limited, while D-CIPHER failures were more often suspended; finals also had a higher share of giveups than quals.Costlimit accounted for 58.10% of ENIGMA failures and 33.51% of D-CIPHER failures; giveup accounted for 26.36% of finals failures versus 23.04% of quals failures.Source: §D.2 · p. 17

D.3. Easy vs. Hard Task Categories for Agents

Agent performance varies by task category: moderate-sized, well-structured reversing and web tasks are comparatively favorable, while pwn, forensics, and crypto remain difficult for distinct operational reasons.

Easier categories

  • GPT-4.1 +ENIGMA reaches 23.40% in reversing, 40.00% in misc, and 16.00% in crypto; GPT-4.1 +D-CIPHER leads web at 26.67%.The authors associate viable exploit chains with moderate problem size and well-structured artifacts.Source: §D.3 · p. 17

Persistently difficult categories

  • Pwn attempts can hang awaiting absent binary output; forensics struggles with huge artifacts; crypto scripts can run indefinitely without termination bounds or complexity reductions.The authors describe these as recurring category-specific difficulties.Source: §D.3 · p. 17

Other categories

  • In other categories, failures are spread across costlimit, suspended, unsolved, and giveup, with no single dominant pattern.This differs from the recurring category-specific patterns described for pwn, forensics, and crypto.Source: §D.3 · p. 18

E. Appendix: Live CTF Evaluation Details

The appendix reports CTFusion results from five Live CTF competitions and provides problem-solving-rate figures for model-agent combinations. It also records competition participation and rankings for several events.

Competition results

  • Figure 8 presents success rates across five LIVE CTFS and NYU CTF BENCH, while Figures 9–12 report problem-solving rates for all model-agent combinations on CUBECTF, UIUCTF, WWCTF, and SEKAICTF.Source: Figure 8 · p. 18, Figure 9 · p. 19, Figure 10 · p. 19

CubeCTF

  • CubeCTF had 1,059 teams and 16 problems; among listed model-agent runs, Claude 3.5-Sonnet + D-CIPHER ranked 90th.GPT-4.1 + ENIGMA ranked 163rd, GPT-4.1 + D-CIPHER and Gemini 2.5-Flash + D-CIPHER ranked 180th; the other two ENIGMA runs did not rank.Source: E. Appendix: Live CTF Evaluation Details · p. 18

UIUCTF

  • UIUCTF 2025 had 985 teams and 31 problems; GPT-4.1 + ENIGMA ranked 335th, and the other listed runs ranked 462nd–642nd.The remaining listed ranks were 535th for GPT-4.1 + D-CIPHER and Claude 3.5-Sonnet + D-CIPHER, 642nd for Claude 3.5-Sonnet + ENIGMA, and 462nd for both Gemini 2.5-Flash runs.Source: E. Appendix: Live CTF Evaluation Details · p. 18

WWCTF and SCRIPTCTF

SEKAICTF

F. Appendix: NYU CTF BENCH Evaluation Details

This appendix presents detailed problem-solving rates for model-agent combinations on the static NYU CTF Bench.

Figures 14–18

Figures 19–23

Figures 24–27

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